Dynamic Transformer Rating (DTR) on large three winding power transformers in the transmission grid of the Netherlands

Dynamic Line Rating (DLR) in the control room. Power transformers (PTs) are critical assets in the transmission grid and are typically operated conservatively, often at ~50% of nominal capacity in N‑1 redundant configurations. By applying thermal modelling, transformer utilization can be safely increased to approximately 65–70% of nominal capacity.

Accurate DTR requires reliable estimation of top‑oil temperature, winding hot‑spot temperature, and insulation ageing. While IEC 60076‑7 provides methodologies for thermal modelling, it is primarily intended for two-winding transformers and does not fully address the three‑winding transformers commonly used by TSOs, where tertiary windings are often connected to shunt reactors. In this paper we provide an adjusted version of the IEC model presented in an open-source Python package which implements three-winding PT modelling as well as ONAN/ONAF switching and tap changer position dependency.

Model validation was performed using factory acceptance test data from an extended heat-run test on a 370 MVA ONAN transformer, as well as historic SCADA data from transformers operating in the TenneT grid. Results show that top‑oil temperatures are modelled with good accuracy, typically within 2–6 K under normal operating conditions. Winding hot‑spot temperatures follow the measured trends but are generally overestimated, providing a conservative safety margin. Larger deviations were observed during cooldown phases indicating areas for further model refinement.

The validated Transformer Thermal Model (TTM) is being integrated into multiple TenneT processes, including day‑ahead operational planning, real‑time SCADA-based monitoring, and long‑term grid planning studies. By using weather forecasts, predicted loading profiles, and contingency scenarios TTM enables higher transformer utilization while respecting thermal and lifetime degradation limits. This dynamic, data-driven approach supports congestion mitigation, improves asset utilization, and facilitates the integration of new customers, thereby strengthening security of supply during the energy transition.